code: def generate_gaps_in_ts(array_without_gaps, gap_dict, gap_value):
      """
      Function for generating gaps with predefined length in the desired indices
      of an one-dimensional array (time series)
  
      :param array_without_gaps: an array without gaps
      :param gap_dict: a dictionary with omissions, where the key is the index in
      the time series from which the gap will begin. The key value is the length
      of the gap (elements). -1 in the value means that a skip is generated until
      the end of the array
      :param gap_value: value indicating a gap in the array
  
      :return: one-dimensional array with omissions
      """
  
      array_with_gaps = np.copy(array_without_gaps)
  
      keys = list(gap_dict.keys())
      for key in keys:
          gap_size = gap_dict.get(key)
          if gap_size == -1:
              # Generating a gap to the end of an array
              array_with_gaps[key:] = gap_value
          else:
              array_with_gaps[key : (key + gap_size)] = gap_value
  
      return array_with_gaps
  
file_path: FEDOT/examples/simple/time_series_forecasting/gapfilling.py
imports: import matplotlib.pyplot
  import numpy
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.node import PipelineNode
  from fedot.utilities.synth_dataset_generator import generate_synthetic_data
  from fedot.utilities.ts_gapfilling import ModelGapFiller, SimpleGapFiller
label_desc: Use image node_id 1 for calling a global function with example usage: generate_gaps_in_ts(array_without_gaps, gap_dict, gap_value) and returns: array_with_gaps